Defining the biomechanical and biological threshold of murine mild traumatic brain injury using CHIMERA (Closed Head Impact Model of Engineered Rotational Acceleration)
Bibliographic record
Abstract
CHIMERA (Closed Head Impact Model of Engineered Rotational Acceleration) is a recently described animal model of traumatic brain injury (TBI) that primarily produces diffuse axonal injury (DAI) characterized by white matter inflammation and axonal damage. CHIMERA was specifically designed to reliably generate a variety of TBI severities using precise and quantifiable biomechanical inputs in a nonsurgical user-friendly platform. The objective of this study was to define the lower limit of single impact mild TBI (mTBI) using CHIMERA by characterizing the dose-response relationship between biomechanical input and neurological, behavioral, neuropathological and biochemical outcomes. Wild-type male mice were subjected to a single CHIMERA TBI using six impact energies ranging from 0.1 to 0.7J, and post-TBI outcomes were assessed over an acute period of 14days. Here we report that single TBI using CHIMERA induces injury dose- and time-dependent changes in behavioral and neurological deficits, axonal damage, white matter tract microgliosis and astrogliosis. Impact energies of 0.4J or below produced no significant phenotype (subthreshold), 0.5J led to significant changes for one or more phenotypes (threshold), and 0.6 and 0.7J resulted in significant changes in all outcomes assessed (mTBI). We further show that linear head kinematics are the most robust predictors of duration of unconsciousness, severity of neurological deficits, white matter injury, and microgliosis following single TBI. Our data extend the validation of CHIMERA as a biofidelic animal model of DAI and establish working parameters to guide future investigations of the mechanisms underlying axonal pathology and inflammation induced by mechanical trauma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".